LFP estimation with dynamic remeshing

Plot LFP from toy neurons

from neuron import h  # , gui
from neuron.units import ms, mV
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.animation import ArtistAnimation
import xcell
from tqdm import trange

import Common
import time
import pickle
import os

from xcell import nrnutil as nUtil
from matplotlib.lines import Line2D

resultFolder = '/tmp'


h.load_file('stdrun.hoc')
h.CVode().use_fast_imem(1)

mpl.style.use('fast')


nRing = 5
nSegs = 5

dmax = 8
dmin = 4

# vids = False
# post = False
nskip = 4

# Overrides for e.g. debugging in Spyder
#args.vids=True
# args.synth=False
# args.folder='Quals/polyCell'
#args.folder='Quals/monoCell'
#args.strat='depth'
#args.nSegs = 101
# args.folder='tst'
#args.nskip=1
#args.nRing=0

ring = Common.Ring(N=nRing, stim_delay=0, dendSegs=nSegs, r=175)
tstop = 40
tPerFrame = 5
barRatio=[9,1]

ivecs, isSphere, coords, rads = nUtil.getNeuronGeometry()


t = h.Vector().record(h._ref_t)
h.finitialize(-65 * mV)
h.continuerun(tstop)

tv = np.array(t)*1e-3
I = 1e-9*np.array(ivecs).transpose()


I = I[::nskip]
tv = tv[::nskip]


analyticVmax = I/(4*np.pi*np.array(rads, ndmin=2))
vPeak = np.max(np.abs(analyticVmax))

imax = np.max(np.abs(I[::nskip]))


coord = np.array(coords)
xmax = 2*np.max(np.concatenate(
    (np.max(coord, axis=0), np.min(coord, axis=0))
))

#round up
xmax=xcell.util.oneDigit(xmax)
if xmax <= 0 or np.isnan(xmax):
    xmax = 1e-4


lastNumEl = 0
lastI = 0

study, setup = Common.makeSynthStudy(resultFolder, xmax=xmax)
setup.currentSources = []
studyPath = study.studyPath

dspan = dmax-dmin


tdata=None

img = xcell.visualizers.SingleSlice(None, study,
                                    tv, tdata=tdata)

for r, c, v in zip(rads, coords, I[0]):
    setup.addCurrentSource(v, c, r)

tmax = tv.shape[0]
errdicts = []

for ii in trange(0, tmax):

    t0 = time.monotonic()
    ivals = I[ii]
    tval = tv[ii]
    vScale = np.abs(analyticVmax[ii])/vPeak

    setup.currentTime = tval

    changed = False


    for jj in range(len(setup.currentSources)):
        ival = ivals[jj]
        setup.currentSources[jj].value = ival

    # Depth strategy
    scale = dmin+dspan*vScale
    dint = np.rint(scale)
    maxdepth = np.floor(scale).astype(int)

    density = 0.2  # +0.2*dfrac

    metricCoef = 2**(-density*scale)


    netScale = 2**(-maxdepth*density)

    changed = setup.makeAdaptiveGrid(
        coord, maxdepth, xcell.generalMetric, coefs=metricCoef)

    if changed or ii == 0:
        setup.meshnum += 1
        setup.finalizeMesh()

        numEl = len(setup.mesh.elements)

        setup.setBoundaryNodes()

        v = setup.iterativeSolve()
        lastNumEl = numEl
        setup.iteration += 1

        study.saveData(setup)  # ,baseName=str(setup.iteration))
        print('%d source nodes' % sum(setup.nodeRoleTable == 2))
    else:
        # TODO: why is reset needed?
        setup.nodeRoleTable[setup.nodeRoleTable == 2] = 0

        v = setup.iterativeSolve()

    dt = time.monotonic()-t0

    lastI = ival

    study.newLogEntry(['Timestep', 'Meshnum'], [
                      setup.currentTime, setup.meshnum])

    setup.stepLogs = []

    errdict = xcell.misc.getErrorEstimates(setup)
    errdict['densities'] = density
    errdict['depths'] = maxdepth
    errdict['numels'] = lastNumEl
    errdict['dt'] = dt
    errdict['vMax'] = max(v)
    errdict['vMin'] = min(v)
    errdicts.append(errdict)


    img.addSimulationData(setup, append=True)

lists = xcell.misc.transposeDicts(errdicts)


xcell.colors.useDarkStyle()
nUtil.showCellGeo(img.axes[0])

ani=img.animateStudy('', fps=30)

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 57%|#####6    | 227/401 [02:38<02:08,  1.36it/s]
 57%|#####6    | 228/401 [02:39<02:07,  1.36it/s]
 57%|#####7    | 229/401 [02:39<02:06,  1.36it/s]
 57%|#####7    | 230/401 [02:40<02:06,  1.36it/s]
 58%|#####7    | 231/401 [02:41<02:05,  1.36it/s]
 58%|#####7    | 232/401 [02:42<02:04,  1.35it/s]
 58%|#####8    | 233/401 [02:42<02:04,  1.35it/s]
 58%|#####8    | 234/401 [02:43<02:03,  1.35it/s]
 59%|#####8    | 235/401 [02:44<02:02,  1.35it/s]2752 elem
27 source nodes

 59%|#####8    | 236/401 [02:45<02:21,  1.17it/s]2864 elem
27 source nodes

 59%|#####9    | 237/401 [02:46<02:35,  1.06it/s]
 59%|#####9    | 238/401 [02:47<02:36,  1.04it/s]
 60%|#####9    | 239/401 [02:48<02:37,  1.03it/s]
 60%|#####9    | 240/401 [02:49<02:37,  1.02it/s]
 60%|######    | 241/401 [02:50<02:36,  1.02it/s]
 60%|######    | 242/401 [02:51<02:35,  1.03it/s]
 61%|######    | 243/401 [02:52<02:33,  1.03it/s]
 61%|######    | 244/401 [02:53<02:32,  1.03it/s]
 61%|######1   | 245/401 [02:54<02:31,  1.03it/s]
 61%|######1   | 246/401 [02:55<02:30,  1.03it/s]
 62%|######1   | 247/401 [02:56<02:29,  1.03it/s]
 62%|######1   | 248/401 [02:57<02:28,  1.03it/s]
 62%|######2   | 249/401 [02:58<02:25,  1.04it/s]2346 elem
27 source nodes

 62%|######2   | 250/401 [02:59<02:24,  1.04it/s]2402 elem
27 source nodes

 63%|######2   | 251/401 [03:00<02:24,  1.04it/s]2780 elem
27 source nodes

 63%|######2   | 252/401 [03:01<02:31,  1.01s/it]
 63%|######3   | 253/401 [03:02<02:27,  1.00it/s]
 63%|######3   | 254/401 [03:03<02:23,  1.03it/s]
 64%|######3   | 255/401 [03:04<02:19,  1.05it/s]
 64%|######3   | 256/401 [03:04<02:16,  1.06it/s]
 64%|######4   | 257/401 [03:05<02:14,  1.07it/s]
 64%|######4   | 258/401 [03:06<02:12,  1.08it/s]
 65%|######4   | 259/401 [03:07<02:10,  1.09it/s]
 65%|######4   | 260/401 [03:08<02:09,  1.09it/s]
 65%|######5   | 261/401 [03:09<02:07,  1.09it/s]
 65%|######5   | 262/401 [03:10<02:07,  1.09it/s]
 66%|######5   | 263/401 [03:11<02:06,  1.09it/s]
 66%|######5   | 264/401 [03:12<02:05,  1.09it/s]
 66%|######6   | 265/401 [03:13<02:03,  1.10it/s]
 66%|######6   | 266/401 [03:14<02:03,  1.10it/s]
 67%|######6   | 267/401 [03:15<02:02,  1.10it/s]
 67%|######6   | 268/401 [03:15<02:01,  1.10it/s]
 67%|######7   | 269/401 [03:16<02:00,  1.10it/s]
 67%|######7   | 270/401 [03:17<01:59,  1.10it/s]
 68%|######7   | 271/401 [03:18<01:58,  1.10it/s]
 68%|######7   | 272/401 [03:19<01:57,  1.10it/s]
 68%|######8   | 273/401 [03:20<01:56,  1.10it/s]
 68%|######8   | 274/401 [03:21<01:55,  1.10it/s]
 69%|######8   | 275/401 [03:22<01:54,  1.10it/s]
 69%|######8   | 276/401 [03:23<01:53,  1.10it/s]
 69%|######9   | 277/401 [03:24<01:52,  1.10it/s]
 69%|######9   | 278/401 [03:25<01:51,  1.10it/s]
 70%|######9   | 279/401 [03:25<01:50,  1.10it/s]
 70%|######9   | 280/401 [03:26<01:49,  1.10it/s]
 70%|#######   | 281/401 [03:27<01:48,  1.11it/s]
 70%|#######   | 282/401 [03:28<01:47,  1.11it/s]
 71%|#######   | 283/401 [03:29<01:46,  1.11it/s]
 71%|#######   | 284/401 [03:30<01:45,  1.11it/s]
 71%|#######1  | 285/401 [03:31<01:44,  1.11it/s]
 71%|#######1  | 286/401 [03:32<01:43,  1.11it/s]
 72%|#######1  | 287/401 [03:33<01:42,  1.11it/s]
 72%|#######1  | 288/401 [03:34<01:41,  1.11it/s]
 72%|#######2  | 289/401 [03:34<01:40,  1.11it/s]
 72%|#######2  | 290/401 [03:35<01:39,  1.11it/s]
 73%|#######2  | 291/401 [03:36<01:38,  1.11it/s]
 73%|#######2  | 292/401 [03:37<01:38,  1.11it/s]
 73%|#######3  | 293/401 [03:38<01:37,  1.11it/s]
 73%|#######3  | 294/401 [03:39<01:36,  1.11it/s]
 74%|#######3  | 295/401 [03:40<01:35,  1.11it/s]
 74%|#######3  | 296/401 [03:41<01:34,  1.11it/s]
 74%|#######4  | 297/401 [03:42<01:33,  1.11it/s]
 74%|#######4  | 298/401 [03:43<01:32,  1.11it/s]
 75%|#######4  | 299/401 [03:43<01:31,  1.11it/s]
 75%|#######4  | 300/401 [03:44<01:31,  1.11it/s]
 75%|#######5  | 301/401 [03:45<01:30,  1.11it/s]2878 elem
27 source nodes

 75%|#######5  | 302/401 [03:46<01:37,  1.01it/s]2976 elem
27 source nodes

 76%|#######5  | 303/401 [03:48<01:42,  1.05s/it]
 76%|#######5  | 304/401 [03:49<01:41,  1.04s/it]
 76%|#######6  | 305/401 [03:50<01:39,  1.04s/it]
 76%|#######6  | 306/401 [03:51<01:37,  1.03s/it]
 77%|#######6  | 307/401 [03:52<01:36,  1.02s/it]
 77%|#######6  | 308/401 [03:53<01:34,  1.02s/it]
 77%|#######7  | 309/401 [03:54<01:33,  1.01s/it]
 77%|#######7  | 310/401 [03:55<01:31,  1.01s/it]
 78%|#######7  | 311/401 [03:56<01:30,  1.00s/it]
 78%|#######7  | 312/401 [03:57<01:29,  1.00s/it]
 78%|#######8  | 313/401 [03:58<01:27,  1.00it/s]
 78%|#######8  | 314/401 [03:59<01:27,  1.00s/it]
 79%|#######8  | 315/401 [04:00<01:24,  1.01it/s]2444 elem
27 source nodes

 79%|#######8  | 316/401 [04:01<01:24,  1.01it/s]2514 elem
27 source nodes

 79%|#######9  | 317/401 [04:02<01:28,  1.05s/it]2864 elem
27 source nodes

 79%|#######9  | 318/401 [04:03<01:30,  1.08s/it]
 80%|#######9  | 319/401 [04:04<01:26,  1.06s/it]2430 elem
27 source nodes

 80%|#######9  | 320/401 [04:05<01:24,  1.04s/it]
 80%|########  | 321/401 [04:06<01:18,  1.02it/s]
 80%|########  | 322/401 [04:07<01:14,  1.06it/s]
 81%|########  | 323/401 [04:08<01:11,  1.09it/s]
 81%|########  | 324/401 [04:08<01:09,  1.11it/s]
 81%|########1 | 325/401 [04:09<01:07,  1.13it/s]
 81%|########1 | 326/401 [04:10<01:05,  1.14it/s]
 82%|########1 | 327/401 [04:11<01:04,  1.15it/s]
 82%|########1 | 328/401 [04:12<01:02,  1.16it/s]
 82%|########2 | 329/401 [04:13<01:02,  1.16it/s]
 82%|########2 | 330/401 [04:14<01:00,  1.16it/s]
 83%|########2 | 331/401 [04:14<00:59,  1.17it/s]
 83%|########2 | 332/401 [04:15<00:59,  1.17it/s]
 83%|########3 | 333/401 [04:16<00:57,  1.17it/s]
 83%|########3 | 334/401 [04:17<00:57,  1.17it/s]
 84%|########3 | 335/401 [04:18<00:56,  1.17it/s]
 84%|########3 | 336/401 [04:19<00:55,  1.17it/s]
 84%|########4 | 337/401 [04:19<00:54,  1.17it/s]
 84%|########4 | 338/401 [04:20<00:53,  1.18it/s]
 85%|########4 | 339/401 [04:21<00:52,  1.18it/s]
 85%|########4 | 340/401 [04:22<00:51,  1.18it/s]
 85%|########5 | 341/401 [04:23<00:50,  1.18it/s]
 85%|########5 | 342/401 [04:24<00:50,  1.18it/s]
 86%|########5 | 343/401 [04:25<00:49,  1.18it/s]
 86%|########5 | 344/401 [04:25<00:48,  1.18it/s]
 86%|########6 | 345/401 [04:26<00:47,  1.18it/s]
 86%|########6 | 346/401 [04:27<00:46,  1.18it/s]
 87%|########6 | 347/401 [04:28<00:45,  1.18it/s]
 87%|########6 | 348/401 [04:29<00:44,  1.18it/s]
 87%|########7 | 349/401 [04:30<00:44,  1.18it/s]
 87%|########7 | 350/401 [04:30<00:43,  1.18it/s]
 88%|########7 | 351/401 [04:31<00:42,  1.18it/s]
 88%|########7 | 352/401 [04:32<00:41,  1.18it/s]
 88%|########8 | 353/401 [04:33<00:40,  1.18it/s]
 88%|########8 | 354/401 [04:34<00:39,  1.18it/s]
 89%|########8 | 355/401 [04:35<00:38,  1.18it/s]
 89%|########8 | 356/401 [04:36<00:38,  1.18it/s]
 89%|########9 | 357/401 [04:36<00:37,  1.19it/s]
 89%|########9 | 358/401 [04:37<00:36,  1.19it/s]
 90%|########9 | 359/401 [04:38<00:35,  1.19it/s]
 90%|########9 | 360/401 [04:39<00:34,  1.19it/s]
 90%|######### | 361/401 [04:40<00:33,  1.19it/s]
 90%|######### | 362/401 [04:41<00:32,  1.19it/s]
 91%|######### | 363/401 [04:41<00:31,  1.19it/s]
 91%|######### | 364/401 [04:42<00:31,  1.19it/s]
 91%|#########1| 365/401 [04:43<00:30,  1.19it/s]
 91%|#########1| 366/401 [04:44<00:29,  1.19it/s]
 92%|#########1| 367/401 [04:45<00:28,  1.19it/s]2864 elem
28 source nodes

 92%|#########1| 368/401 [04:46<00:31,  1.06it/s]2962 elem
28 source nodes

 92%|#########2| 369/401 [04:47<00:32,  1.02s/it]
 92%|#########2| 370/401 [04:48<00:31,  1.02s/it]
 93%|#########2| 371/401 [04:49<00:30,  1.02s/it]
 93%|#########2| 372/401 [04:50<00:29,  1.02s/it]
 93%|#########3| 373/401 [04:51<00:28,  1.02s/it]
 93%|#########3| 374/401 [04:52<00:27,  1.02s/it]
 94%|#########3| 375/401 [04:53<00:26,  1.01s/it]
 94%|#########3| 376/401 [04:54<00:25,  1.01s/it]
 94%|#########4| 377/401 [04:55<00:24,  1.01s/it]
 94%|#########4| 378/401 [04:56<00:23,  1.01s/it]
 95%|#########4| 379/401 [04:57<00:22,  1.01s/it]
 95%|#########4| 380/401 [04:58<00:21,  1.00s/it]
 95%|#########5| 381/401 [04:59<00:19,  1.01it/s]2430 elem
27 source nodes

 95%|#########5| 382/401 [05:00<00:18,  1.01it/s]
 96%|#########5| 383/401 [05:01<00:17,  1.05it/s]2808 elem
28 source nodes

 96%|#########5| 384/401 [05:02<00:17,  1.02s/it]
 96%|#########6| 385/401 [05:03<00:16,  1.01s/it]
 96%|#########6| 386/401 [05:04<00:14,  1.01it/s]
 97%|#########6| 387/401 [05:05<00:13,  1.03it/s]
 97%|#########6| 388/401 [05:06<00:12,  1.04it/s]
 97%|#########7| 389/401 [05:07<00:11,  1.06it/s]
 97%|#########7| 390/401 [05:08<00:10,  1.06it/s]
 98%|#########7| 391/401 [05:09<00:09,  1.07it/s]
 98%|#########7| 392/401 [05:10<00:08,  1.08it/s]
 98%|#########8| 393/401 [05:11<00:07,  1.08it/s]
 98%|#########8| 394/401 [05:12<00:06,  1.08it/s]
 99%|#########8| 395/401 [05:13<00:05,  1.08it/s]
 99%|#########8| 396/401 [05:13<00:04,  1.09it/s]
 99%|#########9| 397/401 [05:14<00:03,  1.08it/s]
 99%|#########9| 398/401 [05:15<00:02,  1.09it/s]
100%|#########9| 399/401 [05:16<00:01,  1.09it/s]
100%|#########9| 400/401 [05:17<00:00,  1.09it/s]
100%|##########| 401/401 [05:18<00:00,  1.08it/s]
100%|##########| 401/401 [05:18<00:00,  1.26it/s]